Polarization losses in reflector antennas

Various definitions for polarization-loss efficiency of Cassegrainian and front-fed reflectors are compared. The effects of flare angle, feed taper and the feed pattern asymmetry on the polarization-loss efficiency are investigated. The definitions based on aperture fields are shown to be inadequate and far fields must be used for calculating the polarization losses.     

How entrepreneurial practices balance art and business: Insights into creative entrepreneurship in the European film industry

Relying on the literature on tensions and contradictions and the theory of practice, coupled with the literature on creative industries, this study uncovers how creative entrepreneurs balance the tension between art and business and respond to other challenges of creative entrepreneurship. The multiple case studies method is adopted by examining founders, cofounders and main employees of European film production companies. We reveal the perception of creative entrepreneurs towards the relationship between art and business. We explore different organizational and industry-level challenges experienced by creative entrepreneurs and uncover the organizational practices they adopt to deal with the identified challenges. The research contributes to the phenomenon of creative entrepreneurship by introducing the practice perspective, to the literature on paradox and contradiction by exploring the micro-foundations of tensions and paradox responses in high-intensity situations and to practice-based studies by investigating a domain-specific practice theory and highlighting the agency of creative entrepreneurs in adapting practices necessary to deal with conflicting demands of creative entrepreneurship.     

Comparative study on classifying human activities with miniature inertial and magnetic sensors

This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.     

An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software.Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of expert’s opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation.An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.     

Pattern optimization of a reflector antenna with planar-array feedsand cluster feeds

We study pattern optimization of a parabolic reflector with planar-array feeds and cluster feeds. The optimization is carried out within the conjugate gradient method (CGM) in which the optimum weight vector of a linearly constrained minimum variance (LCMV) beam former is used as the initial condition. Optimization with the CGM to determine element weights is found to yield better sidelobe suppression and true settlement of desired scan direction compared to the LCMV beam former and conjugate field matching (CFM) techniques for the continuous beam-scanning case. The proposed method is also applied to multibeam reflector-antenna design     

A multilayer incremental neural network architecture for classification

A new multilayer incremental neural network (MINN) architecture and its performance in classification of biomedical images is discussed. The MINN consists of an input layer, two hidden layers and an output layer. The first stage between the input and first hidden layer consists of perceptrons. The number of perceptrons and their weights are determined by defining a fitness function which is maximized by the genetic algorithm (GA). The second stage involves feature vectors which are the codewords obtained automaticaly after learning the first stage. The last stage consists of OR gates which combine the nodes of the second hidden layer representing the same class. The comparative performance results of the MINN and the backpropagation (BP) network indicates that the MINN results in faster learning, much simpler network and equal or better classification performance.     

Airline planning benchmark problems—Part I:: Characterising networks and demand using limited data

This paper is the first of two papers entitled “Airline Planning Benchmark Problems”, aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. While optimisation has made an enormous contribution to airline planning in general, the area suffers from a lack of standardised data and benchmark problems. Current research typically tackles problems unique to a given carrier, with associated specification and data unavailable to the broader research community. This limits direct comparison of alternative approaches, and creates barriers of entry for the research community. Furthermore, flight schedule design has, to date, been under-represented in the optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. This is Part I of two papers taking first steps to address these issues. It does so by providing a framework and methodology for generating realistic airline demand data, controlled by scalable parameters. First, a characterisation of flight network topologies and network capacity distributions is deduced, based on the analysis of airline data. Then a multi-objective optimisation model is proposed to solve the inverse problem of inferring OD-pair demands from passenger loads on arcs. These two elements are combined to yield a methodology for generating realistic flight network topologies and OD-pair demand data, according to specified parameters. This methodology is used to produce 33 benchmark instances exhibiting a range of characteristics. Part II extends this work by partitioning the demand in each market (OD pair) into market segments, each with its own utility function and set of preferences for alternative airline products. The resulting demand data will better reflect recent empirical research on passenger preference, and is expected to facilitate passenger choice modelling in flight schedule optimisation.     

The radiation pattern of a surface loaded ellipsoidal reflector

The radiation patterns of the focus-fed axially symmetric and loaded ellipsoidal antennas inE- andH-planes are calculated by using the generalized method of geometrical theory of diffraction (GTD). The chosen loads are resistive types which are described in terms of surface impedances.     

FPGA Based Implementation and Comparison of Beamformers for CDMA2000

For the integration of smart antennas into third generation code division multiple access (CDMA) base stations, it still remains as a challenging task to implement smart antenna algorithms on programmable processors. In this paper, we study implementations of some CDMA compatible beamforming algorithms, namely least mean square (LMS), constant modulus (CM), and space code correlator (SCC) algorithms, using Xilinx??s Virtex family FPGAs. This study exhibits feasibility of implementing even simple, practical, and computationally small algorithms based on today??s most powerful FPGA technologies. 16 and 32 bits floating point implementations of the algorithms are investigated using both Virtex II and Virtex IV FPGAs. CDMA2000 reverse link baseband signal format is used in the signal modeling. Randomly changing fading and Direction-of-arrivals (DOAs) of multipaths are considered as a channel condition. The implementation results in terms of beamforming accuracy, FPGA resource utilization, weight vector computation time, and DOA estimation error are presented. Beamformer weight vectors using LMS and CM can be computed within less than 20 ??s on Virtex II FPGA and 10 ??s on Virtex IV FPGA, and using SCC it can be achieved within less than 22 ??s on Virtex IV FPGA. These results show that FPGAs provide approximately 500 times faster speed in implementations than our previous work with DSPs.